Mortality rates related to brain and other Central Nervous System (CNS) cancers have held steady over the last three or four decades, despite tremendous advancements in our knowledge about the biology, diagnosis, and treatment of brain cancer. Further progress in early diagnosis and treatment is likely to be associated, in part, with improving computational models that are used ubiquitously for analyzing and segmenting brain tumors. Clinical applications continue to necessitate improved segmentation of hard-to- detect poorly enhanced, multi-foci and small tumors that are surrounded by multiple abnormal tissues such as edema, necrosis and cysts. In addition, computational models need to be improved for handling diffusive boundaries among different tissue types for robust Brain Tumor Segmentation (BTS). Furthermore, in an effort to reduce cognitive sequelae, contemporary protocols employ risk-adapted therapy in which risk stratification is based on the volume of residual tumor after surgical resection and the presence of metastatic disease at diagnosis. Therefore, further improvement in cancer outcomes, particularly among children, is unlikely to be achieved without improved quantitation of tumor volume. Furthermore, replicating advanced computer algorithms across different imaging centers, studies, patient populations (adult and pediatric) and equipment is a persistent problem for the entire field of computational medical imaging. Consequently, the overall hypothesis of this proposed research project is that a robust automatic BTS and other abnormal and normal brain tissue segmentation can be developed for quantitation and tracking of tumor volume which, in turn, will help improve early diagnosis, follow- up and treatment of CNS tumors. The proposed project aims to focus on principled computational modeling using a huge amount of neuroimaging datasets for BTS that are becoming prevalent, especially from the National Cancer Institute's sponsored Brain Tumor Segmentation (BRATS) challenges (http://www.braintumorsegmentation.org). This goal will be accomplished via the following aims: (1) Identify novel features, multiclass (tissue) feature selection and segmentation of hard-to-detect tumors and associated abnormalities using multimodal MRIs from different imaging centers; (2) Enable robust segmentation of tumor, other abnormal and normal tissues and tacking of brain tumor by fusing atlas-based tumor segmentation (ABTS) and feature-based BTS (FBTS); (3) implement software integration into a widely available tool (3D Slicer) available via multiple NIH sponsored Resource Centers such as the Neuroimaging Analysis Center (NAC), the National Alliance for Medical Image Computing (NA-MIC), and the National Center for Image Guided Therapy (NCIGT), for wider dissemination of BTS tool; and (4) Validate and evaluate our integrated BTS tool to quantify improvements in the detectability, sensitivity and specificity, and corresponding errors.